In [1]}, summaries are generated from the mean of the embeddings of
input reviews. A similarity loss encourages the generated summaries to be
close to the review embeddings, while an autoencoder is used to
improve the review embeddings. The intuition that summaries should
capture the consensus opinions continues in [2]}, this
time employing a VAE that can steer towards common information. [3]}
also use VAEs but extend them to produce hierarchical summaries where
some sentences convey general impressions, while other provide specific details
about user experience.
